Lessons Learned from the Analysis of Soldier Collected Blast Data

In recent U.S. military experience, widespread exposure to improvised explosive devices has been implicated in noticeable changes in the incidence of brain injuries inversely related to reduced mortality—thought to be the unintended consequence of increase in exposure to blast wave effects—secondary to improved vital organ protection, improved personal protective equipment. Subsequently, there is a growing need for the development and fielding of fully integrated sensor systems capable of both capturing dynamic effects (i.e., “blast”) on the battlefield—providing critical information for researchers, while providing value to the medical community and leaders—for development of pre-emptive measures and policies. Obtaining accurate and useful data remains a significant challenge with a need for sensors which feed systems that provide accurate interpretation of dynamic events and lend to an enhanced understanding of their significance to the individual. This article describes lessons learned from a data analysis perspective of a collaborative effort led by a team formed at Georgia Tech Research Institute to develop a “sensor agnostic” system that demonstrates full integration across variant platforms/systems. The system is designed to allow digital and analog time/frequency data synchronization and analysis, which facilitated the development of complex multimodal modeling/algorithms.

• Brad Fain

Fain, W.B., Phelps, S., Medda, A., "Lessons Learned from the Analysis of Soldier Collected Blast Data." Military Medicine, 180, 201-206, March 2015.

• Center for Advanced Communications Policy
• Other (Non-IAC) Department